Monitoring disease activity in systemic lupus erythematosus with single-molecule array digital ELISA quantification of serum interferon-α.

ObjectiveNo simple or standardized assay is available to quantify interferon- (IFN) in routine clinical practice. Single-molecule array (Simoa) digital enzyme-linked immunosorbent assay (ELISA) technology enables direct IFN quantification at attomolar (femtogram per milliliter [fg/ml]) concentrations. This study was undertaken to assess IFN digital ELISA diagnostic performances to monitor systemic lupus erythematosus (SLE) activity. MethodsIFN concentrations in serum samples from 150 consecutive SLE patients in a cross-sectional study were determined with digital ELISA and a functional biologic activity assay (bioassay). According to their Safety of Estrogens in Lupus Erythematosus National Assessment version of the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) flare composite scores, patients were divided into groups with inactive SLE (SLEDAI score of <4 or clinical SLEDAI score of 0) or active SLE (SLEDAI score of 4 or clinical SLEDAI score of >0), and into groups with no flare or mild/moderate flare or severe flare. ResultsBased on serum samples from healthy blood donors, the abnormal serum IFN level threshold value was 136 fg/ml. Next, using receiver operating characteristic curves for an SLE patient series that was widely heterogeneous in terms of disease activity and organ involvement, the threshold IFN value associated with active disease was determined to be 266 fg/ml. The digital ELISA-assessed serum IFN level was a better biomarker of disease activity than the Farr assay because its specificity, likelihood ratio for positive results, and positive predictive value better discerned active SLE or flare from inactive disease. The digital ELISA was more sensitive than the bioassay for detecting low-abnormal serum IFN concentrations and identifying patients with low disease activity. ConclusionDirect serum IFN determination with a highly sensitive assay might improve monitoring of clinical SLE activity and selection of the best candidates for anti-IFN treatment.